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Machine Learning | ![]() |
Namespaces | |
namespace | vigra::detail |
Classes | |
class | RandomForest |
class | RandomForestOptions |
Options object for the random forest. More... | |
Learning | |
Following functions differ in the degree of customization allowed | |
template<class U, class C1, class U2, class C2, class Split_t, class Stop_t, class Visitor_t, class Random_t> | |
double | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, Visitor_t visitor, Split_t split, Stop_t stop, Random_t const &random) |
learn on data with custom config and random number generator | |
template<class U, class C1, class U2, class C2, class Split_t, class Stop_t, class Visitor_t, class Random_t> | |
double | onlineLearn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, int new_start_index, Visitor_t visitor_, Split_t split_, Stop_t stop_, Random_t &random, bool adjust_thresholds=false) |
template<class U, class C1, class U2, class C2, class Split_t, class Stop_t, class Visitor_t, class Random_t> | |
void | reLearnTree (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, int treeId, Visitor_t visitor_, Split_t split_, Stop_t stop_, Random_t &random) |
prediction | |
template<class U, class C> | |
LabelType | predictLabel (MultiArrayView< 2, U, C >const &features) |
template<class U, class C> | |
LabelType | predictLabel (MultiArrayView< 2, U, C > const &features, ArrayVectorView< double > prior) const |
predict a label with features and class priors | |
template<class U, class C, class Stop> | |
LabelType | predictLabel (MultiArrayView< 2, U, C >const &features, Stop &stop) const |
predict a label given a feature. | |
template<class U, class C1, class T, class C2> | |
void | predictLabels (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &labels) const |
predict multiple labels with given features | |
template<class U, class C1, class T, class C2, class Stop> | |
void | predictLabels (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &labels, Stop &stop) const |
template<class U, class C1, class T, class C2> | |
void | predictProbabilities (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &prob) const |
predict the class probabilities for multiple labels | |
template<class U, class C1, class T, class C2, class Stop> | |
void | predictProbabilities (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &prob, Stop &stop) const |
predict the class probabilities for multiple labels | |
template<class T1, class T2, class C> | |
void | predictProbabilities (OnlinePredictionSet< T1 > &predictionSet, MultiArrayView< 2, T2, C > &prob) |
template<class U, class C1, class U2, class C2, class Split_t, class Stop_t, class Visitor_t, class Random_t> | ||||
void reLearnTree | ( | MultiArrayView< 2, U, C1 > const & | features, | |
MultiArrayView< 2, U2, C2 > const & | response, | |||
int | treeId, | |||
Visitor_t | visitor_, | |||
Split_t | split_, | |||
Stop_t | stop_, | |||
Random_t & | random | |||
) | [inherited] |
template<class U, class C1, class U2, class C2, class Split_t, class Stop_t, class Visitor_t, class Random_t> | ||||
double learn | ( | MultiArrayView< 2, U, C1 > const & | features, | |
MultiArrayView< 2, U2, C2 > const & | response, | |||
Visitor_t | visitor, | |||
Split_t | split, | |||
Stop_t | stop, | |||
Random_t const & | random | |||
) | [inherited] |
learn on data with custom config and random number generator
features | a N x M matrix containing N samples with M features | |
response | a N x D matrix containing the corresponding response. Current split functors assume D to be 1 and ignore any additional columns. This is not enforced to allow future support for uncertain labels, label independent strata etc. The Preprocessor specified during construction should be able to handle features and labels features and the labels. |
visitor | visitor which is to be applied after each split, tree and at the end. Use RF_Default for using default value. |
split | split functor to be used to calculate each split use rf_default() for using default value. | |
stop | predicate to be used to calculate each split use rf_default() for using default value. | |
random | RandomNumberGenerator to be used. Use rf_default() to use default value. |
template<class U, class C, class Stop> | ||||
LabelType predictLabel | ( | MultiArrayView< 2, U, C >const & | features, | |
Stop & | stop | |||
) | const [inherited] |
predict a label given a feature.
features,: | a 1 by featureCount matrix containing data point to be predicted (this only works in classification setting) | |
stop,: | early stopping critierion |
template<class U, class C> | ||||
LabelType predictLabel | ( | MultiArrayView< 2, U, C > const & | features, | |
ArrayVectorView< double > | prior | |||
) | const [inherited] |
predict a label with features and class priors
features,: | same as above. | |
prior,: | iterator to prior weighting of classes |
template<class U, class C1, class T, class C2, class Stop> | ||||
void predictProbabilities | ( | MultiArrayView< 2, U, C1 >const & | features, | |
MultiArrayView< 2, T, C2 > & | prob, | |||
Stop_t & | stop | |||
) | const [inherited] |
predict the class probabilities for multiple labels
features | same as above | |
prob | a n x class_count_ matrix. passed by reference to save class probabilities | |
stop | earlystopping criterion |
template<class U, class C1, class T, class C2> | ||||
void predictProbabilities | ( | MultiArrayView< 2, U, C1 >const & | features, | |
MultiArrayView< 2, T, C2 > & | prob | |||
) | const [inherited] |
predict the class probabilities for multiple labels
features | same as above | |
prob | a n x class_count_ matrix. passed by reference to save class probabilities |
template<class U, class C1, class T, class C2> | ||||
void predictLabels | ( | MultiArrayView< 2, U, C1 >const & | features, | |
MultiArrayView< 2, T, C2 > & | labels | |||
) | const [inherited] |
predict multiple labels with given features
features,: | a n by featureCount matrix containing data point to be predicted (this only works in classification setting) | |
labels,: | a n by 1 matrix passed by reference to store output. |
© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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